Read counts data

Load and counts file

oriCountsFile <- read.table(file = fileCounts)
countsFile <- oriCountsFile
countsFile <- janitor::row_to_names(countsFile, row_number=1)
names(countsFile)[1] <- "miR"
head(countsFile)
countsFile$miR <- str_extract(countsFile$miR, "[^|]+") 
countsFile

Update the IDs

length(unique(countsFile$miR))
[1] 2588

Fetch the mirbase22 IDs and sequences

dim(mrbse)
[1] 2656    2

Merge this with the updated IDs

Merge this with the data

length(mergedData[,"Name"])
[1] 2588

Quintize the data

For each column: assign each miRNA a value 1-5 depending on which of the 20th percentiles it falls into. If the value is 0, it remains a 0

```r
# Given a column, assign a number to each element from 0-5. All
# 0s get a 0, and the rest get a value according to the 20th percentile
# that it falls in among the non-zero values.
quintize <- function(vec) {
  qntls <- c(0, quantile(vec[which(vec != 0)], 0.2*(1:4)))
  vec2 <- sapply(vec, function(x) {
    if (x == 0 || is.na(x)) return(0)
    else return(max(which(x > qntls)))
  })
  return(vec2)
}

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```r
```r
mergedData[,3:ncol(mergedData)] = lapply(mergedData[,3:ncol(mergedData)], FUN = function(y){as.numeric(y)})
quintizedData <- apply(mergedData[,-c(1:2)], 2, quintize)
quintizedData <- cbind(mergedData[,1:2], quintizedData)
quintizedData[1:5,1:5]

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### Combine the replicates

Get the sample mapping file


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```r
```r
mapping <- read.table(\./Samples_mapped.txt\)
mapping <- janitor::row_to_names(mapping, row_number=1)


#annotation <- read.table(\./microRNA_samples.txt\)
#annotation <- janitor::row_to_names(annotation, row_number=1)
#names(annotation)[2] <- \Disease\
#sampleMeta <- read.table(\rigoutsos_sampleInfo.txt\, header = T, sep = \\t\)
#meta <- read.table(\meta.txt\, header = T, sep = \\t\)
#meta$patient.primary_pathology.tumor_tissue_site

# Create sample - tissue mapping
#mapping <- sampleMeta[,names(sampleMeta) %in% c(\Sample\,\tissue_or_organ_of_origin\)]
#names(mapping)[2] <- \Tissue\
#mapping$Sample <- chartr('-', '.', mapping$Sample) # replace '-' with '.' in Sample


# Add tissue info to annotation, based on sample-tissue mapping
#annotation$Tissue <- mapping$Tissue[match(annotation$Sample, mapping$Sample)]

#Sample <- annotation$Sample
#missingTissue <- Sample[which(is.na(annotation$Tissue))]
#missingTissue

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Create Disease, Tissue, and Group (Tissue_Disease) columns


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```r
```r
mapping$Tissue <- str_replace_all(mapping$tumor_tissue_site, \ \, \.\)
mapping$Tissue <- tolower(mapping$Tissue)
#mapping$Group <- apply(mapping[,c(ncol(mapping)-1,ncol(mapping))], 1, function(x) paste(x[1], x[2], sep=\_\))
mapping$Group <- apply(mapping[,c(5,2)], 1, function(x) paste(x[1], x[2], sep=\_\))
head(mapping)

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Grouping: Combine replicates (samples that have the same type)


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```r
sid <- sort(mapping$Sample_ID[!mapTrue])
Error in sort(mapping$Sample_ID[!mapTrue]) : object 'mapping' not found

Ensure the terms are standardized

Ensure that all the group names appear in the ontology or the corrections file.

Get the ontology and correction files

```r
ont <- read.table(\C:/Users/gitta/Documents/MirDIP_counts/ontology.txt\, header = F, sep = \\t\)
corr <- read.table(\C:/Users/gitta/Documents/MirDIP_counts/corrections.txt\, header = T, sep = \\t\)

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Aim to have 0 as a result, meaning all terms are either already in the ontology or the correction file. Or else: update correction file or ontology file, or both.


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```r
```r
trms <- unique(unlist(strsplit(names(combinedData)[3:ncol(combinedData)], \_\))) # Splits the composite terms that contain both tissue+disease
if (length(which(trms %in% union(corr[,1], unique(unlist(ont[,c(1,3)]))))) != 0){
  trms[-which(trms %in% union(corr[,1], unique(unlist(ont[,c(1,3)]))))] # Which terms aren't in the ontology or the corrected terms
} else {
  trms
}

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character(0)




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Correct current terms that need to be corrected.

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```r
colnames(combinedData) <- sapply(colnames(combinedData), function(z) {
  y <- strsplit(z, "_")[[1]]
  retVal <- sapply(y, function(x) {
    if (x %in% corr$currentTerm) {
      return(corr$correctedTerm[match(x, corr$currentTerm)])
    } else {
      return(x)
    }
  })
  return(paste(retVal, collapse = "_"))
})
Error in is.data.frame(x) : object 'combinedData' not found

Convert to long format

Add the Canonical column and Source column

```r
data <- combinedData # all tissues have to exist in ontology or correction files
data$Canonical <- sapply(data$Seq, function(x) if (x %in% mrbse$Seq) return(T) else return(F))
data$Source <- \Francisco*\
head(data)
data <- data[,c(1,2,ncol(data)-1,ncol(data),3:(ncol(data)-2))] # Set columns alignment
#head(data)

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Convert to long format


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```r
```r
data_long <- melt(data, id.vars=c(\Name\, \Seq\, \Canonical\, \Source\))

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Binarization

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```r
```r
data_long$Binary <- sapply(data_long$value, function(x) if (x == 0) return(0) else return(1))
names(data_long) <- c(\miR\, \Seq\, \Canonical\, \Source\, \Tissue\, \Scale\, \Binary\)

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### Write to file


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```r
```r
write.table(data_long, \./francisco_longData.txt\, sep = \\t\, row.names = F, col.names = T, quote = F)
write.table(unique(data_long[,1:3]), \./francisco_miRNAs.txt\, sep = \\t\, row.names = F, col.names = T, quote = F)
writeLines(as.character(unique(data_long$Tissue)), \./francisco_tissues.txt\)

```

---
title: "Processing TCGA expression data"
author: "Gitta Ekaputeri, Anne-Christin Hauschild"
date: "29/08/2022" 
output: html_notebook
---


```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(stringr)
library(miRBaseConverter)
library(reshape2)

#### Used Files: 
fileCounts<-'./microRNA_TCGA_preprocessed.txt'
fileMetaData<-"./Samples_mapped_microRNAs.txt"
fileOntology<-"../ontology2.txt"
fileCorrections<-"../corrections.txt"
fileRemTissues<-"../removeTissues.txt"
```

### Read counts data

Load and counts file
```{r}
oriCountsFile <- read.table(file = fileCounts)
countsFile <- oriCountsFile
countsFile <- janitor::row_to_names(countsFile, row_number=1)
names(countsFile)[1] <- "miR"
head(countsFile)
countsFile$miR <- str_extract(countsFile$miR, "[^|]+") 
countsFile
```

### Update the IDs 

```{r}
update <- miRNAVersionConvert(countsFile$miR)
head(update)
length(unique(countsFile$miR))
length(unique(update[,"OriginalName"]))
length(unique(update[,"TargetName"]))

```

Fetch the mirbase22 IDs and sequences

```{r}
mrbse <- read.table("../mature_homo-sapiens_dataframe.txt", header = T, sep = "\t")
mrbse <- mrbse[-c(1,2),] # Remove the first two rows that don't match up with mirbase and are the only duplicates, and are the only sequences with T's for some reason.
dim(mrbse)
head(mrbse)
```

Merge this with the updated IDs

```{r}
mrbse2 <- merge(mrbse, update, by.x = 1, by.y = 2, all.x = F, all.y = T)
mrbse2 <- mrbse2[,-4]
dim(mrbse2)
head(mrbse2)
```


Merge this with the data

```{r}
mergedData <- merge(mrbse2, countsFile, by.x = 3, by.y = 1, all.x = T, all.y = F)
mergedData <- mergedData[,-1] # Remove the original (old) IDs
mergedData[1:5,1:5]
dim(mergedData)
head(mergedData)
length(mergedData[,"Name"])
```


### Quintize the data

For each column: assign each miRNA a value 1-5 depending on which of the 20th percentiles it falls into. If the value is 0, it remains a 0

```{r}
# Given a column, assign a number to each element from 0-5. All
# 0s get a 0, and the rest get a value according to the 20th percentile
# that it falls in among the non-zero values.
quintize <- function(vec) {
  qntls <- c(0, quantile(vec[which(vec != 0)], 0.2*(1:4)))
  vec2 <- sapply(vec, function(x) {
    if (x == 0 || is.na(x)) return(0)
    else return(max(which(x > qntls)))
  })
  return(vec2)
}
```

```{r}
mergedData[,3:ncol(mergedData)] = lapply(mergedData[,3:ncol(mergedData)], FUN = function(y){as.numeric(y)})
quintizedData <- apply(mergedData[,-c(1:2)], 2, quintize)
quintizedData <- cbind(mergedData[,1:2], quintizedData)
quintizedData[1:5,1:5]
```


### Combine the replicates

Get the sample mapping file

```{r}
mapping <- read.table(fileMetaData)
mapping <- janitor::row_to_names(mapping, row_number=1)
head(mapping)

#annotation <- read.table("./microRNA_samples.txt")
#annotation <- janitor::row_to_names(annotation, row_number=1)
#names(annotation)[2] <- "Disease"
#sampleMeta <- read.table("rigoutsos_sampleInfo.txt", header = T, sep = "\t")
#meta <- read.table("meta.txt", header = T, sep = "\t")
#meta$patient.primary_pathology.tumor_tissue_site

# Create sample - tissue mapping
#mapping <- sampleMeta[,names(sampleMeta) %in% c("Sample","tissue_or_organ_of_origin")]
#names(mapping)[2] <- "Tissue"
#mapping$Sample <- chartr('-', '.', mapping$Sample) # replace '-' with '.' in Sample


# Add tissue info to annotation, based on sample-tissue mapping
#annotation$Tissue <- mapping$Tissue[match(annotation$Sample, mapping$Sample)]

#Sample <- annotation$Sample
#missingTissue <- Sample[which(is.na(annotation$Tissue))]
#missingTissue

```


Create Disease, Tissue, and Group (Tissue_Disease) columns

```{r}
mapping$Tissue <- str_replace_all(mapping$tumor_tissue_site, " ", ".")
head(mapping)
mapping$Tissue <- tolower(mapping$Tissue)
head(mapping)
mapping$Group <- apply(mapping[,c(5,2)], 1, function(x) paste(x[1], x[2], sep="_"))
head(mapping)
```


Grouping: Combine replicates (samples that have the same type)

```{r}
uniqueTissues <- unique(mapping$Group)
quintizedData2 <- quintizedData
head(quintizedData2[,1:10])
dim(quintizedData2)
dim(mapping)
mapTrue<- mapping$Sample_ID %in% names(quintizedData2)
length(mapTrue)
sum(mapTrue)

#test <- quintizedData2[,(names(quintizedData2) %in% mapping$Sample_ID)]
#test2 <- lapply(uniqueTissues, function(x) {
#  df <- as.data.frame(test[,mapping$Sample_ID[which(mapping$Group == x)]])
#  return(rowMeans(df))
#})


combinedData <- lapply(uniqueTissues, function(x) {
  df <- as.data.frame(quintizedData2[,mapping$Sample_ID[which(mapping$Group == x)]])
  return(rowMeans(df))
})

names(combinedData) <- uniqueTissues
combinedData <- do.call(cbind, combinedData)
combinedData <- cbind(quintizedData2[,1:2], combinedData)
head(combinedData)
cleantissues<-colnames(combinedData)
cleantissues<- sapply(cleantissues, FUN=function(g){ 
                                            a<-gsub( "(", ".", g, fixed = TRUE); 
                                            a<-gsub( ")", ".", a, fixed = TRUE); 
                                            a<-gsub( "-", ".", a, fixed = TRUE); 
                                            a<-gsub( "/", ".", a, fixed = TRUE); 
                                            a<-gsub( "..", ".", a, fixed = TRUE); 
                                            a<-gsub("..", ".", a, fixed = TRUE); 
                                            a<-gsub("._", "_", a, fixed = TRUE); 
                                            return(a) } )
#(data_long[43:48,"Tissue"])
(unique(cleantissues)[43:48])
colnames(combinedData)<-cleantissues
head(combinedData)

## Remove Tissues:
dim(combinedData)
rmTissues <- unlist(read.table(fileRemTissues, header = F, sep = "\t"))
n<-"lower.abdominal.pelvic.other.please.specify_sarcoma"
rmList<- sapply(colnames(combinedData), FUN=function(n){ l<-unlist(strsplit(n, "_"));  
                                                         l<- c(l %in% rmTissues, l=="NA")
                                                         return(sum(l)>0)  }) 
sum(rmList)
combinedData<- combinedData[,!rmList]
dim(combinedData)

```


### Ensure the terms are standardized
Ensure that all the group names appear in the ontology or the corrections file.

Get the ontology and correction files

```{r}
ont <- read.table(fileOntology, header = F, sep = "\t")
corr <- read.table(fileCorrections, header = T, sep = "\t")
```

Aim to have 0 as a result, meaning all terms are either already in the ontology or the correction file. Or else: update correction file or ontology file, or both.

```{r}
  trms <- unique(unlist(strsplit(names(combinedData)[3:ncol(combinedData)], "_"))) # Splits the composite terms that contain both tissue+disease
  if (length(which(trms %in% union(corr[,1], unique(unlist(ont[,c(1,3)]))))) != 0){
    trms[-which(trms %in% union(corr[,1], unique(unlist(ont[,c(1,3)]))))] # Which terms aren't in the ontology or the corrected terms
  } else {
    trms
  }
```
Correct current terms that need to be corrected.
```{r}
colnames(combinedData) <- sapply(colnames(combinedData), function(z) {
  y <- strsplit(z, "_")[[1]]
  retVal <- sapply(y, function(x) {
    if (x %in% corr$currentTerm) {
      return(corr$correctedTerm[match(x, corr$currentTerm)])
    } else {
      return(x)
    }
  })
  return(paste(retVal, collapse = "_"))
})
```

### Convert to long format
Add the Canonical column and Source column

```{r}
data <- combinedData # all tissues have to exist in ontology or correction files
data$Canonical <- sapply(data$Seq, function(x) if (x %in% mrbse$Seq) return(T) else return(F))
data$Source <- "TCGA"
data <- data[,c(1,2,ncol(data)-1,ncol(data),3:(ncol(data)-2))] # Set columns alignment
head(data)
```


Convert to long format

```{r}
data_long <- melt(data, id.vars=c("Name", "Seq", "Canonical", "Source"))
```

Binarization
```{r}
data_long$Binary <- sapply(data_long$value, function(x) if (x == 0) return(0) else return(1))
names(data_long) <- c("miR", "Seq", "Canonical", "Source", "Tissue", "Scale", "Binary")




```


### Write to file

```{r}
write.table(data_long, "./tcga_longData.txt", sep = "\t", row.names = F, col.names = T, quote = F)
write.table(unique(data_long[,1:3]), "./tcga_miRNAs.txt", sep = "\t", row.names = F, col.names = T, quote = F)
writeLines(as.character(unique(data_long$Tissue)), "./tcga_tissues.txt")
```
